CHAPTER TWO INFORMATION · PDF fileCHAPTER TWO INFORMATION TECHNOLOGY ... not just supply chain management. ... companies in an extended or virtual supply chain. In §2.3,

© Copyright 2000, Jeremy F. Shapiro. All rights reserved. 2-1

CHAPTER TWO

INFORMATION TECHNOLOGY

2.1 INTRODUCTION

Rapid developments in information technology (IT), which is defined as both

computing and telecommunications, have affected all aspects of business, not just supply

chain management. During the 1990’s, sales of IT grew to the extent that it has become

the largest industry in the United States exceeding construction, food products and

automotive manufacturing.1 As shown in Figure 2.1, business investment in hardware and

software has increased exponentially since the mid-1980’s, reaching a torrid pace by the

end of the 1990’s, with no end in sight.2 Moreover, these investments do not include sky-

rocketing fees paid for IT consulting services.

The creation and management of corporate databases has been facilitated by

widespread implementation of Enterprise Resource Planning (ERP) systems. These

systems offer the promise of transactional data bases that are standardized across the

company, thereby facilitating integration of supply chain activities. Models are playing

an increasing role in helping managers extract effective supply chain plans from these

databases. Nevertheless, the purposes of such models, and their potential for improving a

company’s competitive advantage, are not yet fully understood by many supply chain

managers.

In §2.2, we provide a brief overview of developments in ERP and e-commerce

systems from the perspective of supply chain management. These systems are primarily

Transactional IT concerned with acquiring, processing and communicating raw data about

the company’s supply chain, and with the compilation and dissemination of


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reports summarizing these data. They also facilitate communication among different

companies in an extended or virtual supply chain. In §2.3, we compare Transactional IT

with Analytical IT, which is created to assist managers in making supply chain decisions.

Analytical IT employs models constructed from supply chain decision databases that

are derived from the company’s transactional databases. Analytical IT is comprised of

these supply chain decision databases, plus modeling systems and programs linking

corporate databases to the supply chain decision databases.

In theory, the modeling systems in a company’s Analytical IT and their decision

databases should be organized in a suite of inter-connected applications for strategic,

tactical and operational planning. This hierarchy of modeling systems is discussed in

detail in §2.4. In practice, very few companies have yet come close to achieving a

complete suite. Nevertheless, we believe there is value in laying out a comprehensive

structure for these modeling systems and discussing linkages among the various

components.

In §2.5, we discuss issues connected with enhancing legacy systems for and legacy

thinking about supply chain modeling. These issues are a preview of behavioral research,

which we discuss in Chapter 12, into barriers inhibiting rational decision making using

models. The chapter concludes with §2.6 where we present final thoughts about the

information revolution, especially as it relates to supply chain management.

2.2 DEVELOPMENTS INENTERPRISE RESOURCE PLANNING SYSTEMS

AND ELECTRONIC COMMERCE

The development of information technology (IT) for managing and communicating

transactional data has been a primary focus of computer scientists and information

technologists for over 40 years.3 Figure 2.1 reveals the great expansion during the 1990’s

of managerial interest and investment in hardware and software for these purposes. Still,

even with the advent of Enterprise Resource Planning (ERP) systems and e-commerce,

we would be naïve to suppose that companies have achieved permanent solutions to their

data management problems.


It is beyond the aim of this book to discuss these software and hardware

developments in great detail. Rather, we will be concerned in this chapter with the synergy

between IT and supply chain management using modeling systems.4 Today’s IT enables

the development of such systems, but paths to their successful implementation remain

unclear in most companies. Although scholars and consultants often use expressions such

as “the company must (can, should) optimize supply chain decisions,” details about how

such analysis can be performed are often missing. Moreover, managers do not yet fully

realize how models can provide a comprehensive, high level view of the tangled forest of

their transactional supply chain data. Based on their growing interest in modeling systems,

we expect managers will soon become much better informed. By participating in processes

to extract, aggregate, extrapolate, and otherwise transform transactional data into input

data required by modeling systems to support decision making, managers will gain a fresh

and important perspective on how to exploit IT advances.

In this section, we review recent developments in ERP systems and e-commerce.

Our discussion of e-commerce is divided into separate examinations of business-to-

consumer and business-to-business e-commerce. Business-to-business linkages via the

Internet require extended or new ERP systems that facilitate communication among

companies of diverse sizes and missions.

ERP SYSTEMS

An ERP system is comprised of software and hardware that facilitates the flow of

transactional data in a company relating to manufacturing, logistics, finance, sales and

human resources. In principle, all business applications of the company are integrated in

an uniform system environment that accesses a centralized database residing on a common

platform. Common and compatible data fields and formats are used across the entire

enterprise. Moreover, data is entered once and only once ensuring that all applications

make consistent use of these data.

In 1997, worldwide sales of ERP systems and related services were estimated to

exceed $10 billion. Of this total, approximately 50% was for software, 30% for

installation, training, customization, and 20% for maintenance and upgrades.5 The large

percentage and absolute amount spent on installation, training and customization reflects


the need for significant business process re-design to bring systems and practices across

diverse functions into alignment. In addition to improving human data collection and

communication processes, re-engineering should, but does not always, include cleansing

and tightening of data definitions: for example, a reduction in the number of SKU’s to

those that are truly distinct.

Implementing ERP systems has proven unexpectedly difficult in many

companies. Numerous articles have appeared in magazines and journals offering guidelines

for avoiding the worst headaches. Even when an implementation project is well managed,

the result can be disappointing due to inherent limitations of current ERP systems. These

include6

• Imposed conformity – the ERP system imposes rigid requirements on data and

processes that often inhibit the way the company can operate its business

• Inability to employ software from multiple vendors – the company cannot integrate

modules, including modeling systems, from multiple vendors with the monolithic ERP

system acquired from the primary vendor

• Incompatibility of ERP systems across the supply chain – the company cannot easily

integrate supply chain databases with vendors and customers, especially those who

are too small to afford a massive ERP implementation

Current thinking is that these problems will be overcome by new ERP systems that are

modular and Web-enabled.7 Individual modules for transactional data management and

modeling analysis, often developed by third-party vendors, will be bolted onto ERP

systems using middleware, which provides standard interfaces for the modules.

The rapid growth in e-commerce has magnified these deficiencies of ERP systems.

The expectation is that Internet-driven re-engineering will require integrating business

processes across corporate boundaries. Modular, flexible ERP systems will be essential

for implementing inter-enterprise information systems for supply chains comprised of

several companies of varying sizes and cultures. Moreover, since e-commerce is so new,

Internet companies will need the capability to modify their ERP systems as new

conditions emerge.


E-COMMERCE

Communication over the Internet is characterized by easy accessibility, low cost

to use, and speed. These features are the result of astonishing developments in recent

years in computer networks, processing speed, data storage capabilities, software,

display technology, and user interfaces. The linking of people and companies due to e-

commerce has opened up exciting new marketing opportunities as well as new processes

for improving supply chain management. Direct business-to-consumer marketing over the

Internet of products such as clothes, groceries, and PCs is an emerging concept that has

become red hot. Business-to-business communication over the Internet is also expanding

rapidly. We discuss these two new developments in the paragraphs that follow. We

conclude by examining Internet systems and processes that facilitate the creation of

supply and demand markets for commodities.

Business-to-Consumer E-Commerce

Business-to-consumer e-commerce is a new method of retailing that puts the

consumer in direct contact with a grocery, clothing, or PC company offering products.

These Internet companies faces a host of new marketing and sales challenges including

• devising graphics to attractively display physical products on the website

• pricing products to gain market share, reflect supply chain costs, or some other

criteria that change radically with evolving markets

• extrapolating sales patterns from initial markets to new markets

• identifying demographics of website customers

• devising acceptable and sustainable customer service criteria

• devising strategies to retain customers

• selecting the number and range of products to offer that the website and the

supply chain can support

• connecting website sales to physical inventory

• providing security for payment by credit cards


Innovations of business-to-customer Internet companies occur largely on the demand side

of their businesses, although changes to conventional thinking are also needed for supply

chain management.

Despite the excitement of business-to-customer Internet companies, their sales are

still only a small percentage of total retailing sales. Moreover, they are not projected to

make serious inroads in the near future in most industries. For example, the market for

home delivery of groceries is forecast to reach $3.5 billion by 2002, which represents only

0.7% of total grocery sales projected for that year. Similarly, clothes and accessories

acquired over the Internet are forecast to represent only 1.6% of total industry sales in

2002.8

The logistics of business-to-customer Internet companies is driven by the classical

order fulfillment principle: Deliver the correct product to the correct location at the

correct time for a competitive price. These companies have only just begun to realize the

full importance of this principle, and how difficult it is to effectively link their web-based

marketing and sales activities to their order fulfillment activities. In simple terms, an

Internet company has two choices. They can build their own warehouses and manage

their own distribution systems or they can hire third party logistics companies to handle

distribution for them.

Either option can prove costly. An Internet company can expect to spend

between $60 Million to $80 Million in constructing a one million square foot warehouse,

a size that is needed for a high volume company, especially during peak periods. In

addition to needing access to large amounts of capital, the company obviously has to be

confident that they will sustain a justifiable level of sales volume over the foreseeable

future. Alternatively, they can employ a third party distributor who will charge them

roughly 10 percent of gross sales to fulfill their orders.9

An additional problem for a business-to-consumer Internet company is the need

to link their website with the system that manages their inventory. This connection is

critical if customers are to receive real-time information about product availability. Again,

if the Internet company has sufficient capital, in addition to building warehouses, it can

develop customized integrated systems with seamless connections among the website, the

inventory management system, and other systems needed to manage the company’s


supply chain. Such an integrated system is discussed in §10.4 where we examine supply

chain management and modeling systems in a company making home deliveries of

groceries. If, however, the Internet company decides to employ a third party distributor,

it runs the risk of trying to link its website and order entry system with the distributor’s

software for inventory management and tracking. Incompatibilities with the distributor’s

systems might cause headaches as large as those experienced in developing a customized

integrated system.

Finally, an Internet company must anticipate escalating demands from customers

for higher levels of service. For example, customers seeking home delivery of groceries

will prefer companies offering same day delivery with tight time windows over those

offering next day delivery with looser time windows. For manufactured products such as

PC’s, customers will prefer companies offering to more fully customize their purchases

over those offering little or no customization. To meet these increasing pressures, faster

and more powerful data management and modeling systems are required.

In summary, supply chain management of business-to-consumer Internet

companies is subject to serious economies of scale in paybacks from investments in

warehouses, inventories, and integrated data management and modeling systems. Due to

high and increasing customer service expectations, these economies of scale may be even

more pronounced than those experienced by traditional retailers. It suggests that we may

soon see a serious reduction in the number of Internet companies in a given industry as

mergers and acquisitions allow companies to achieve volumes justifying capital

investment in brick and mortar and integrated systems.

Business-to-Business E-Commerce

Although investors are enthralled with the promise of business-to-consumer e-

commerce, the potential impact of business-to-business e-commerce on supply chain

management is much larger. In principle, business-to-business systems can provide

effective inter-enterprise communication of data and plans for manufacturing and

distribution across virtual supply chains in many industries. This might allow companies

to move work across corporate boundaries, reduce cycle times by direct interconnection,


and develop collaborative forecasts. But many barriers must be overcome if the promise

of virtual supply chains is to be realized.

First, multiple companies operating a virtual supply chain must have standardized

definitions and meanings of data. A decision to shift production of a product from one

company to a second in the virtual supply chain because the unit production cost is lower

in the second company assumes that this cost is consistently defined and measured across

the two companies. Moreover, systems must be in place for seamless integration of data.

Such requirements are, of course, no different than those addressed and allegedly met by

ERP systems. And, as we discussed above, ERP companies are currently active in

developing modules and middleware that they believe will allow standardization and

efficient communication to be achieved across virtual supply chains.

Second, virtual supply chains assume a level of inter-company coordination that is

often not achieved today among business units of the same company. For example, we

cannot presume that a manufacturer of consumer durables will be willing to share

sensitive cost data with a major OEM distributor of these products, especially when the

manufacturer sells to other distributors and to its own franchised stores. Another issue is

how companies in a virtual supply chain will agree to split cost savings realized from

improved business-to-business communication and supply chain management.

Third, faster communication of data does not automatically lead to better decision

making. As we discuss in the following sections of this chapter, competitive supply chain

management cannot be achieved merely by rapid and myopic response to today’s supply

chain needs. Optimization modeling systems are required to unravel the complex

interactions and ripple effects that make supply chain management difficult and

important.

Procurements over the Internet, Spot Markets and Auctions

A variety of electronic markets have emerged over the past five years where

commodities, collectibles, and other products are bought and sold. Our interest is

primarily in business-to-business sales over the Internet, which could exceed $66 billion

during the year 2000.10 This development has been called Internet-based procurement

(IBP). It is concerned with direct procurement of specific parts and components needed


for production by buyers, and indirect procurement of commodities and products not

customized for production such as generic parts, office supplies, maintenance, and so on.

Electronic procurement is developing along three dimensions11

• Seller-side sites – Suppliers place their catalogs and spec sheets on their Web sites.

The functionality of the sites may be expanded to include search capabilities and

electronic commerce functionality such as credit card sales. Boise Cascade and Office

Depot have sites of this type.

• Buyer-side sites – Buyers have installed software, which is available from software

providers, allowing them to read and standardize vendor catalogs. The software also

allows the creation, routing, approval and submission of orders.

• Third-party sites – These are neutral sites that serve as marketplaces where buyers

and sellers can link up. They provide catalog information from several sellers and

usually capabilities supporting direct sales between the buyer and the seller. These

sites are usually specific to certain industries such as specialty chemicals or

pharmaceuticals.

Most of the transactions performed on these sites are indirect procurements of

commodity products. Market researchers have found that Internet purchasing has led to

improved corporate-wide purchasing strategies, lower transaction costs, and improved

productivity among buyers.

Direct procurement over the Internet by manufacturing firms can be more

complicated because the required parts and components may require some customization.

Moreover, factors beyond cost, such as quality, on-time delivery, and supplier flexibility,

may be very important. Richer, more flexible software solutions are needed, but they

require customization and can be very expensive. For this reason, some industries are

seeking to establish common standards for their suppliers. For example, a trade

association in the automotive industry commissioned the implementation of a standards-

based network, the Automotive Network eXchange (ANX). Recently, Ford and General

Motors announced that their suppliers will soon be required to be connected to and use

this network. The expectation is that communication over ANX with suppliers in the


second, third and fourth tiers will take hours, rather than weeks using the current

hodgepodge of systems.

A related development is electronic markets where buyers and sellers participate

in auctions of goods and services. The intention of these auctions is to enhance economic

efficiency through aggregation and matching.12 Aggregation refers to the assembly of very

large numbers of buyers and sellers. Matching refers to dynamic processes whereby

buyers are able to link up with sellers offering products that best match their needs.

These developments suggest the possible emergence of spot markets for industrial

products and services, including contract manufacturing and shipping, that will seriously

affect supply chain strategies in many industries.

2.3 COMPARISON OF TRANSACTIONAL ITAND ANALYTICAL IT

As we just discussed, widespread implementation of ERP and e-commerce

systems offer the promise of homogeneous, transactional databases that will facilitate

managerial decision-making. In many companies, however, the scope and flexibility of

installed ERP systems has been less than desired or expected. New ERP systems that are

modular and Web-enabled are scarcely past the drawing board stage, but we can expect to

see significant improvements over the next three to five years.

In any event, competitive advantage cannot be gained simply through faster and

cheaper communication of data. As many managers have come to realize, ready access to

transactional data does not automatically lead to better decision making. In reality,

Enterprise Resource Planning is a misnomer because it fails to provide insights into

decisions affecting “resource planning.” The guiding principle for overcoming these

deficiencies is

To effectively apply IT in managing its supply chain, a company must distinguish

between the form and function of Transactional IT and Analytical IT

Transactional IT is concerned with acquiring, processing and communicating raw data

about the company’s supply chain, and with the compilation and dissemination of


reports summarizing these data. The data may originate from internal sources, such as a

general ledger system or a manufacturing process control system, or it may originate from

external sources, such as an order placed over the internet or trucking rates of a common

carrier accessed by EDI.

By contrast, Analytical IT evaluates supply chain planning problems using

descriptive and normative models. Descriptive models, such as demand forecasting or

managerial accounting models, describe how supply chain activities, costs, constraints and

requirements may vary in the future. Normative, or optimization, models, such as a linear

programming model for capacity planning, describe the space of supply chain options

over which the supply chain manager wishes to optimize his/her decisions. Normative

models are constructed from supply chain decision databases using descriptive models

and data aggregation methods. These decision databases are discussed in detail in

Chapter 6.

Analytical IT is not very dissimilar in meaning from the term decision support

system (DSS). We have avoided using this term because it has come to connote an

unsystematic application of ad hoc methods to the analysis of business decision

problems. The implication is that each new decision problem requires the design and

implementation of a new model and a new DSS. We take an opposite viewpoint by

arguing that optimization models provide a rigorous, rich and coherent discipline for

constructing and deploying general-purpose tools. These tools are the cornerstone of

Analytical IT for integrated supply chain management. Optimization models are

discussed in detail in Chapters 3, 4, and 5.

Differences between Transactional IT and Analytical IT can be contrasted across a

number of aspects. In the paragraphs that follow, we discuss six contrasting aspects.

Aspect: Time frame addressed

Transactional IT: Past and present

Analytical IT: Future

Transactional IT focuses on communicating, storing and reporting on data

describing the company’s current supply chain operations. These data are added to


historical databases. They may describe internal operations, such as the orders shipped

today from a distribution center, or the tons of product manufactured today by a machine

in a plant. They may also describe the environment in which the firm does business, such

as industry sales for the previous quarter. Analytical IT picks up where Transactional IT

leaves off by extrapolating data into the future, and analyzing one or more scenarios to

identify effective decisions.

Aspect: Purpose

Transactional IT: Communications

Analytical IT: Forecasting and decision making

As we just discussed, Transactional IT communicates data describing the

company’s current and past supply chain activities, while Analytical IT seeks to forecast

scenarios of the future and optimize decisions associated with these scenarios.

Uncertainties about the future depend on the length of the decision problem’s planning

horizon, and the nature of the industry in which the firm competes. The uncertainties

may be slight for short-term decisions such as the selection of routes for shipping

completed orders to customers over the next week. In such cases, operational plans may

safely be developed from a single scenario of the future. At the other extreme, strategic

plans stretching out five years, or more, may entail considerable uncertainty and require

evaluation of many scenarios using a model.

Aspect: Business Scope

Transactional IT: Myopic

Analytical IT: Hierarchical

By its nature, Transactional IT is myopically concerned with current transactions

and the compilation of histories based on them. Analytical IT addresses future decisions

through a hierarchy of decision problems at all levels of planning, operational, tactical and

strategic. Thus, in the short-term, it may address myopic operational decisions, while, in


the long-term, it may address global facility location and mission decisions that include

aggregate descriptions of these same operational decisions to be made at a much later time.

Aspect: Nature of databases

Transactional IT: Raw and lightly transformed objective data

Analytical IT: Raw, moderately and heavily transformed data that is both objective

and judgmental

The databases created by Transactional IT are derived from raw data that is stored

in formats that leave the data unchanged or “lightly transformed”. We use this expression

to define, admittedly in a vague way, the limits of Transactional IT. It refers to operations

on data that are easy to understand although, for large data sets, the resulting

transformations may require considerable processing time. An example of lightly

transformed data is a report based on aggregate product categories of the cost and volume

of SKU’s acquired last quarter by a retailing company. Another example is the

computation of average costs for shipping full truckloads last month from all company

sourcing points to all market zones. By contrast, optimization models employed by

Analytical IT require data inputs derived from raw data that may involve significant

transformations. For example, the mapping of general ledger costs at each production

plant into direct and indirect product and process costs, and indirect plant costs, all of

which may be fixed or variable, with variable costs that may be linear or nonlinear.

Databases created and used by Analytical IT tools will also contain judgmental

data about the company’s supply chain. An example might be constraints that mitigate

production risk by limiting any plant to making no more than 75% of next year’s

forecasted demand of certain products. Another example is a constraint limiting to 400

miles the distance from each market to the distribution center that serves it.

These differences between transactional and analytical databases reflect the

differences in business scope discussed above. From a bottom-up perspective, the

handoff from Transactional IT to Analytical IT occurs when the company seeks to

optimize operational plans for the short-term future. Data that is irrelevant to operational

decision making, such as addresses where invoices for customer orders should be mailed,


is stripped from transactional databases, while the remaining data are fed to appropriate

supply chain decision databases. Some modeling practitioners estimate that 80%, or more,

of a transactional database is irrelevant to decision making and model construction.

As the planning horizon of decision problems to be evaluated by Analytical IT

stretches further into the future, the link between Transactional IT and Analytical IT

becomes more complex. The broader scope of longer term decision problems requires

aggregations of transactional data to provide the model and the decision makers with a

better view of their “planning forest” rather than the “trees” representing details of the

company’s operations. The aggregations must be reversed when longer-term plans are

translated into operational plans.

Aspect: Response time for queries

Transactional IT: Real-time

Analytical IT: Real-time and batch processing

Computing speeds have reached a point where users expect instantaneous, or at

least very fast, responses to data queries. This is especially true for Transactional IT

responsible for retrieving raw data from corporate databases. Of course, some

applications involve databases that are so large that rapid response to queries requires a

network of dedicated computers. For example, to support system-wide queries about

inventory, large retailing companies stocking 50,000 SKU’s across 500 stores and 10

distribution centers employ a data decomposition scheme in which multiple computers,

each dedicated to a subset of the company’s product line, are accessed through a server.

For certain types of Analytical IT, such as a system producing forecasts of

demand for a single product by weeks over the coming year, the response may also be

nearly instantaneous. Although the underlying model may perform a non-trivial amount

of number-crunching in determining the forecast, computation is fast enough that the user

does not perceive an appreciable delay. For other types of applications, such as the

determination of a vehicle routing schedule for daily deliveries to 1000 customers, 15

minutes, or longer, may be required to generate and solve an optimization model on a

high-end PC. Considerable number-crunching in a batch mode is required for this


application because it entails comprehensive evaluation of a complex system of customers

and proposed routes, rather than myopic analysis of data pertaining to a single customer

or a single route.

The distinction between myopic analysis and comprehensive systems analysis of

supply chain decision problems and their requirements for response time, is not yet

widely understood or appreciated. Many problems, such as the vehicle routing problem

just cited, could be analyzed by myopic methods in real-time, or in no more than a few

seconds. For complex problems, however, the plans identified by myopic methods will be

markedly inferior to those determined by a global model and optimization method. If

he/she understood this difference, the distribution manager would certainly be willing to

wait several minutes to obtain a plan produced by a batch run of an optimization model

that delivers all orders using 10% fewer trucks. Moreover, considerable refinement by a

human analyst may be needed to make the solution produced by a myopic method

acceptable for implementation.

The importance of response time diminishes, but does not disappear, as the scope

of the supply chain problems to be analyzed moves from daily, operational concerns to

tactical and strategic ones. Analysis of tactical decisions affecting activities over the next

few days or months must still be made in a timely fashion. Strategic planning may require

evaluation of many scenarios to complete a study within a tight timeframe, thereby

constraining the response time available for extracting useful answers from an

optimization model describing a single scenario.

Aspect: Implications to business process redesign

Transactional IT: Substitute for or eliminate inefficient human effort

Analytical IT: Coordinate overlapping managerial decisions

The impact of IT on business process redesign is an immense subject that we

discuss briefly here. We will return to it in §12.6. At this juncture, we simply point out

that Transactional IT and Analytical IT have qualitatively different impacts on the

organization and management of a company’s supply chain. Transactional IT has allowed

communication of data describing operational business processes to be automated and


made more efficient. It has also provided managers with timely data to make better-

informed intuitive decisions about short-term operations, although some decisions could

be improved if they were based on model results.

Analytical IT allows supply chain decisions to be integrated across managerial

responsibilities, and across levels of planning, but to be fully exploited, it entails major

organizational change. Although such changes are underway in many companies, their

ultimate nature and extent depends on future IT and organizational developments about

which we speculate in Chapter 12. In summary, Analytical IT seeks to systematically

identify opportunities for improving the management of the company’s supply chain by

functional and inter-temporal integration of decisions, whereas Transactional IT addresses

myopic opportunities for such improvement.

2.4 HIERARCHY OF SUPPLY CHAIN SYSTEMS

In the previous section, we emphasized the importance of inter-temporal

integration of supply chain activities, as well as their functional and geographical

integration. Inter-temporal integration can be fully achieved only by the application of a

suite of modeling systems to the gamut of strategic, tactical and operational planning

decision problems faced by the company. These Analytical IT systems are linked to

overlapping supply chain decision databases created from data provided by Transactional

IT systems. Companies offering ERP software have realized this need. They are actively

expanding their offerings to include modeling systems for all levels of planning, either by

developing such systems themselves or by acquiring companies with modeling software.

Components of the Supply Chain System Hierarchy

In Figure 2.2, we display the Supply Chain System Hierarchy comprised

of six types of optimization modeling systems and four transactional systems responsible

for inter-temporal, functional, and geographical integration of supply chain activities in a

manufacturing and distribution company with multiple plants and distribution centers. As

shown in the Figure, the six types of optimization modeling systems are Analytical IT


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and the four other systems are Transactional IT. Strictly speaking, the Demand

Forecasting and Order Management System is a hybrid with analytical capabilities for

forecasting demand and transactional capabilities for handling customer orders.

We have also shown an important linkage between external data management

systems maintained by the company’s customers and suppliers and the company’s

enterprise transactional data. Recent advances in e-commerce offer the promise to

streamline and enhance such communication. They also increase the need for modeling

systems, especially to support operational decision-making across multiple firms.

The Supply Chain System Hierarchy in Figure 2.2 is hypothetical. To the best of

our knowledge, no company has implemented and integrated all nine types of systems,

although many companies have implemented several of them. Moreover, the components

and structure of the Hierarchy may appear arbitrary and we would expect that they might

be modified for specific applications. Still, based on the author’s participation in scores of

projects in which supply chain modeling systems have been developed, the Hierarchy

represents the most likely configuration needed to analyze strategic, tactical and

operational supply chain planning problems in a firm that both manufactures and

distributes products.

The transactional and scheduling systems in the System Hierarchy represent the

bottom-up thrust in supply chain management. IT developments are the driving force for

innovations in this area, with business process re-design as a natural consequence. The

area is red hot with annual sales of software in the hundreds of millions of dollars and

growing rapidly.

Distinctions among the transactional and scheduling systems displayed in Figure

2.2 have become blurred. Software companies offering ERP systems have either acquired

or entered into alliances with companies offering operational modeling systems. Similarly,

some DRP systems include modules for vehicle scheduling and forecasting. For our

purposes here, we choose to maintain separation between the form and function of the

modeling and transactional systems.

In seeking to better manage their operations, a company must decide if it wishes

to acquire off-the-shelf systems, or to develop customized systems implemented by its

internal IT staff, by outside system developers, or some combination of the two.


Customized systems, if they are implemented in an effective and timely manner, are more

likely to provide the company with competitive advantage than the acquisition of off-the-

shelf systems.

The number and diversity of off-the-shelf systems for operational supply chain

management is increasing. Most off-the-shelf scheduling systems to support MRP and

DRP decision-making use heuristics that are less formal, and less effective in identifying

demonstrably good operational plans, than the optimization models we envision for the

Production Scheduling and Logistics Optimization Modeling Systems. They rely heavily

on graphical user interaction in developing scheduling solutions, often placing too high a

burden on historical rules and the judgment of a human scheduler to extract a good

operational plan from a complex data set. We believe the unified optimization

methodology discussed and illustrated in Chapter 5 has the potential to provide modeling

systems with superior performance.

Strategic optimization modeling systems in the System Hierarchy reflect the top-

down thrust in supply chain management. The driving force is senior management's need

for strategic analysis in the face of globalization of the company's markets and supply

chains, and competition in cost and service. A typical strategic planning study is being

performed by consultants who employ an optimization modeling system. They exercise

the system to provide management with quantitative insights into the evolution and re-

design of their supply chains, and answers to "what if" questions about the long-term

future.

The study mode for applying these systems is often very useful, but short-

sighted. Since the strategic supply chain problems evaluated by the modeling systems

almost certainly will not disappear at the end of the study period, the company would

profit greatly by making modeling analysis a permanent part of its strategic review

processes. Nevertheless, senior management encounters several organizational barriers

when it tries to use a strategic modeling system on a regular basis.

First, the company must commit to the design and implementation of IT

procedures for collecting and updating the supply chain decision database. It must also

commit to the creation and training of analysts who will devote a significant portion of

their time in performing on-going strategic evaluations. Second, training entails a complete


transfer of the modeling system technology from the modeling system developers and, in

many cases, the external consultants who perform the study, to the company. Finally, the

company must implement processes whereby senior management works with analysts

and lower level managers in performing modeling studies, reviewing results, and

implementing plans that they suggest.

Long-term and short-term tactical supply chain planning have thus far been

mainly ignored by managers and consultants. They are the most difficult areas in which to

develop better planning methods, based in part on optimization modeling systems.

Despite the growing number of applications of strategic optimization modeling systems,

we have seen few initiatives to move down the Hierarchy to develop and use these

systems for related tactical planning problems. The lack of interest by companies that

have successfully used optimization modeling systems for strategic studies in extending

them to tactical modeling applications is frustrating for modeling practitioners. From a

technical perspective, such extensions are easy to accomplish because the model and the

supply chain decision database will be validated during the study.

Still, this reluctance is not surprising since repetitive use of a Tactical

Optimization Modeling System requires considerable business process re-design. Tactical

applications also require the development and upkeep of supply chain decision databases,

which, as we already observed, are not yet well understood. Despite the difficulties, we

are optimistic about the ultimate breakthrough of tactical modeling applications because

the potential rewards are so great. Limited applications have demonstrated that a

manufacturing or distribution company can expect to reduce its total supply chain costs

by 5%, or more, by implementing plans identified by a modeling system. The tool is also

valuable in helping management adjust to unexpected changes in its business environment,

such as a fire at a company plant or a strike at a key vendor.

Starting from the bottom-up, the following are synopses of the capabilities of each

system type.

Enterprise Resource Planning (ERP) System: The ERP System manages the

company’s transactional data on a continuous, real-time basis. This System standardizes

the company’s data and information systems for order entry, financial accounting,


purchasing, and many other functions, across multiple facilities and business units.

Despite the claim implied by the term ERP, effective “resource planning” across the

“enterprise” can be identified only by optimization models created using data from the

ERP System.13

Materials Requirement Planning (MRP) System: Analysis with the MRP System

begins with a master production schedule of finished products needed to meet demand

in each period of a given planning horizon. Using these data, along with a balance on

hand of inventories of raw materials, work-in-process and finished goods, a bill of

materials description of the company’s product structures, and machine production

data, the MRP System develops net requirements by period of raw materials and

intermediate products to be manufactured or ordered from vendors to meet demand for

finished products. Products at all stages of manufacturing are analyzed by the MRP

System at the SKU level.14

Distribution Requirements Planning (DRP) System: Analysis with a DRP System

begins with forecasts of finished products to be transported, a balance on hand of

inventories of these products at plants and distribution centers, and inventory

management data such as safety stock requirements, replenishment quantities, and

replenishment times. In conjunction with the Distribution Scheduling Optimization

Model Systems, the DRP System then schedules in-bound, inter-facility, and out-bound

shipments through the company’s logistics network, taking into account a wide range of

transportation factors such as vehicle loading and routing, consolidations, modal choice,

channel selection, and carrier selection. Products throughout the logistics network are

analyzed by the DRP System at the SKU level.15

Demand Forecasting and Order Management System: This System combines data

about current orders with historical data to produce requirements for finished products to

be met by the operational, tactical and strategic plans. For operational and short-term

tactical planning, an important challenge is to manage the transition from forecasts, which


have a significant degree of uncertainty, to orders, which have much less uncertainty.

Longer-term planning requires linkages to data on industry and economic factors that have

a high degree of uncertainty.16

Production Scheduling Optimization Modeling Systems: These are modeling

systems located at each plant in the company’s supply chain that address operational

decisions such as the sequencing of orders on a machine, the timing of major and minor

changeovers, or the management of work-in-process inventories. The models must fit the

environment, which may be discrete parts manufacturing, process manufacturing, job-

shop scheduling, or some hybrid. 17 A single facility may require different modeling

systems at different stages of manufacturing; for example, fine paper production at a mill

involves process manufacturing to produce mother rolls of paper followed by job-shop

scheduling to produce the final products.

Distribution Scheduling Optimization Modeling Systems: The manufacturing and

distribution company faces a variety of vehicle and other scheduling and operational

planning problems. In addition to local delivery of products to customers, some

companies must decide on a short-term basis which distribution center should serve each

market based on inventory availability. As with production scheduling, distribution

scheduling problems and models vary significantly across industries.18

Production Planning Optimization Modeling Systems: Each plant in the

company’s supply chain uses its version of this optimization modeling system to

determine a master production plan for the next quarter for each stage of manufacturing,

along with resource levels and resource allocations for each stage, that minimize

avoidable manufacturing costs. As part of the optimization, the model also determines

work-in-process inventories, major machine changeovers, and make-or-buy decisions.

The models used by this System will be multi-period as well as multi-stage. Therefore,

for reasons of computational necessity, products are aggregated into product families.

These aggregations are reversed when the System hands off the master schedule to the


plant’s Production Scheduling and MRP Systems. Although many papers have

appeared in the academic literature discussing production planning models with this

broad scope, few modeling systems based on them have yet been implemented.19

Logistics Optimization Modeling System: This System determines a logistics master

plan for the entire supply chain that analyzes how demand for all finished products in all

markets will be met over the next quarter. Specifically, it focuses on the assignment of

markets to distribution centers and other facilities responsible for sourcing them. Its goal

is to minimize avoidable transportation, handling, warehousing and inventory costs across

the entire logistics network of the company, while meeting customer service requirements.

Again, for reasons of computational necessity, finished products are aggregated into

product families and markets are aggregated into market zones. These aggregations are

reversed when the System hands off the master schedule to the plant’s Distribution

Scheduling and DRP Systems. This type of optimization modeling system has also not

yet been widely implemented.

Tactical Optimization Modeling System: This System determines an integrated

supply/manufacturing/distribution/inventory plan for the company’s entire supply chain

over the next 12 months. Its goal may to be minimize total supply chain cost of meeting

fixed demand, or to maximize net revenues if product mix is allowed to vary. Raw

materials, intermediate products and finished products are aggregated into product

families. Similarly, markets are aggregated into market zones. This is another type of

optimization modeling system that has not yet been widely implemented.20

Strategic Optimization Modeling System: This System is used to analyze resource

acquisition and other strategic decisions faced by the company such as the construction of

a new manufacturing facility, the break-even price for an acquisition, or the design of a

supply chain for a new product. Its goal may be to maximize net revenues or return on

investment. A number of off-the-shelf packages, with varying degrees of modeling

capabilities, are available for this type of application.21


FREQUENCY OF ANALYSIS, CYCLE TIMES AND RUN TIMES OFSUPPLY CHAIN SYSTEMS

In the following sub-sections, we discuss interactions among systems immediately

adjacent to one another in the Supply Chain System Hierarchy depicted in Figure 2.2.

Before delving into these details, we need to examine how and when these systems are

applied. To this end, Table 2.1 reviews several timing features of each system:

• frequency of analysis – the number of times each year, quarter or month that

managers and planners use the system

• planning time – how long it takes to complete analysis of the planning problems

with the system each time it is used

• run time – batch time required for each run of the system

The times shown in Table 2.1 are representative of the systems in the Hierarchy. They

may vary significantly from company to company. The frequency of analysis will be

much longer than once a week for the Tactical Supply Chain Modeling System, and much

shorter than once a quarter for the Production Scheduling Modeling System. The planning

horizons of the Modeling Systems, the MRP System, and the DRP System overlap. This

facilitates coordination and communication among them.

The column labeled Model Structure refers to the number of periods incorporated

in models generated by the modeling system. For example, a strategic optimization model

will typically be a one period, or snapshot model, where the period is one year. A

production planning model might be a six period model where the first four periods are

weeks and the final two periods are months.

As we descend in Table 2.1 from strategic to operational systems, the planning

horizon becomes shorter while the description of time in the model structures, as

measured by the number of periods in the models, becomes more detailed. In addition,

the objective function shifts from net revenue maximization to avoidable cost

minimization as we move from strategic to operational planning. Although, net revenue


Feat

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of

Ana

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maximization should be sought at all planning levels, the company may have few options

to affect revenue at the operational level.

One can expect or hope that, in the coming years, net revenue maximization will

work its way down the Hierarchy as the company’s management improves its abilities to

integrate supply chain and demand management decisions. For example, the Production

Scheduling Modeling System could be employed to maximize short-term net revenues by

identifying which customized orders to accept or reject, or to determine prices for such

orders so as to guarantee healthy margins. Such a change in using this System would

require changes in business processes to support both the requisite analysis and

negotiations with customers.

COMMUNICATION AMONG SUPPLY CHAIN SYSTEMS OF DATA AND

DECISIONS

In the paragraphs that follow, we discuss interactions among the systems in the

Supply Chain System Hierarchy. In effecting these interactions, decisions determined by

the Modeling Systems become input data to other systems with which they

communicate.

ERP, MRP, DRP and Forecasting and Order Management Systems

Figure 2.3 depicts interactions among the ERP, MRP, DRP and Forecasting and

Order Management Systems. Although we have shown them as separate systems, they

could be viewed as a single ERP entity dedicated to acquiring, communicating and managing

transactional data requirements across the company. The MRP and DRP Systems that are

one level up from the ERP System develop and disseminate detailed production and

distribution schedules. A separate MRP System is employed in each plant, whereas the

DRP System addresses distribution operations across the entire company. These Systems

are mainly transactional programs that translate master production and distribution

schedules into detailed schedules. The Systems also keep track of actual production and

distribution data. The typical planning horizon for these schedules is 7 to 28 days.

The ERP system provides the MRP and DRP systems with detailed data about

costs, capacities and equipment. It also passes data about orders to the Forecasting and


Operational Supply Chain Systems

Figure 2.3

distributionmanagers

salesmanagers

Forecasting and Order

ManagementSystem

MaterialsRequirements

PlanningSystem

DistributionRequirements

PlanningSystem

EnterpriseResource Planning System

productionmanagers

CostsCapacitiesMachine

performancedata

Detailedproductionschedules

CostCapacitiesVehicle

performancedata

Detailedtransportation

schedules

Detailedfinished goods inventories at distribution

centers

Orders

Detailedwip and

finished goods inventories at

plants


Order Management System, which in turn passes orders and forecasts to the MRP and

DRP Systems. The company’s production and distribution managers use detailed

schedules developed by these Systems to execute the company’s operational plans.

These data, along with data about inventories are also passed to the ERP System for

tracking, accounting and control purposes.

MRP and Production Scheduling Modeling Systems

Without the Production Modeling System, users of the MRP System must

determine master schedules and available capacities in an ad hoc way based on historical

rules-of-thumb. Although the typical MRP System has rudimentary tools intended to

assist company planners in determining schedules, they leave much to be desired. For

example, it might compute capacity loadings implied by the master schedule, but cannot

adjust the master schedule if the loadings exceed available capacity.

In short, the MRP System cannot identify a short-term schedule, required resource

levels, and their allocations, which minimize total operational costs over the short-term

planning horizon. Moreover, it cannot assist schedulers in determining a feasible schedule,

or which orders to delay, when manufacturing capacity is tight. As a result, in using the

MRP System without the Production Scheduling Modeling System, production managers

can only muddle through the scheduling process by using trial-and-error methods. For this

reason, the company needs a Modeling System that employs optimization models and

methods to determine an effective production schedule over a 13 week planning horizon,

with particular attention paid to the next 4 weeks, which span the 28 day horizon of the

MRP system. The typical model generated by this System looks out 13 weeks to ensure

stability to the detailed plan for the next 28 days that ultimately will be executed according

to the MRP System.

As shown in Figure 2.4, the optimization model determines production set-ups,

production runs, discretionary resource levels, work-in-process and finished goods

inventories so as to minimize avoidable costs associated with attempting to meet

customer orders. We say “attempting” to meet customer orders because the company

may encounter order schedules that cannot be met. In such an event, based on implicit and


Linkages between Short-term Tacticaland Operation Production Planning Systems

in a Plant

Figure 2.4

By production family: Master production schedule Wip inventories Finished goods inventories at plantBy work centers: Resource levels Resource allocationsOrders to be backlogged

ProductionSchedulingModelingSystem

Aggregation Disaggregation

MaterialsRequirements

PlanningSystem

Detailed production, cost and inventory dataDetailed order and demand dataSemi-permanent data about machine performance and labor requirements

productionplanner

productionmanagers


explicit penalties associated with late deliveries, the production scheduling model assists

production managers in determining which orders will be completed and shipped late.

The links between the Production Scheduling Modeling System and the MRP

System involve aggregation when data are fed upward from the MRP System to the

Modeling System, and disaggregation when data are fed downward from the Modeling

System to the MRP System.22 Upward aggregation entails aggregation of products into

products families and detailed time dependent data, such as scheduled maintenance or

machine changeovers, into aggregate time dependent data, such as the week in which these

events will take place. Downward disaggregation entails translation of production

schedules and inventories of product families into details regarding individual products. It

also entails translation of the aggregate timing of time-sensitive decisions into more

detailed timing. The disaggregation transformation is essentially an inversion of the

aggregation transformation with rules-of-thumb applied to ensure that the resulting details

are efficient and best satisfy downstream production and customer requirements. Thus, in

designing the upward aggregation, care must be taken to ensure that the corresponding

downward disaggregation can be easily and accurately carried out.

DRP and Logistics Modeling Systems

Figure 2.5 shows the relationship between the DRP System and the Logistics

Modeling System. Unlike the production systems just discussed that separately analyze

each plant, these Systems analyze decisions across the company’s entire logistics network,

which might include several plants and distribution centers, and several hundred markets.

They also coordinate company transportation activities with those of the vendors.

Otherwise, the company’s motivation for implementing and deploying the Logistics

Modeling System is the same as that for the Production Scheduling Modeling System.

Without such a System, distribution managers using the DRP System must muddle through

the short-term scheduling of transportation movements and the operations of distribution

centers to support them. For example, the DRP system has the capability to heuristically

optimize daily vehicle loading and routing decisions, but cannot determine which


Linkages between Short-term Tacticaland Operation Distribution Planning Systems

Figure 2.5

By production family: Master transportation schedule Finished goods inventories at DC'sFor each DC: Operating schedule Resource levels Resource allocations

LogisticsModelingSystem


DistributionRequirements

PlanningSystem

Detailed transportation capacity and cost dataDetailed inventory dataDetailed costs and capacity data for DC's

logisticsmanagers

logisticsmanagers


distribution centers should serve each market, and how operations at the distribution

centers should be scheduled, so as to minimize short-term costs.

The Logistics Modeling System determines a master transportation schedule that

includes in-bound shipments of raw materials and parts to the plants, inter-plant shipments

of intermediate and finished products, shipments of finished products to distribution

centers, and out-bound shipments to the markets of finished products. Decisions about the

latter shipments fine-tune the longer-term assignment of markets to distribution centers

determined by the Tactical Modeling System. In addition, the Logistics Modeling System

makes modal choices for large shipments based on timing considerations; for example, a

choice between a single large rail shipment from a plant to a distribution center, or many

truck movements spread out over a month’s time.

Production Scheduling Modeling Systems, Logistics Modeling System, TacticalModeling System, Demand Forecasting and Order Management System

The Tactical Modeling System is the lowest level system in the Hierarchy that

analyzes decisions across the company’s entire supply chain. As shown in Figure 2.6, it

passes aggregate details about the optimal supply chain plan for each of the three months

of the immediate quarter to the Production Scheduling Modeling Systems, one in each

plant, and to the Logistics Modeling System. The details of this plan are disaggregated to

provide guidelines for the Production Scheduling Modeling System and the Logistics

Modeling System. Disaggregation may entail refinement of product families and the

timing of resource planning decisions. Schedules developed by the lower level systems are

fed back to the Tactical Supply Chain Modeling System by reversing these

disaggregations. These schedules reflect short-term commitments that the higher level

system treats as fixed and given.

Unlike the interactions discussed previously, we have shown linkages, which are

two directional, between these Modeling Systems and the Demand Forecasting and Order

Management System. In particular, the Modeling Systems receive order and forecasting

information from this System, while the Tactical Supply Chain Modeling System sends

suggested product mix strategies to this System. Marketing and sales personnel can use


Lin

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these strategies in revising their plans so as to enhance the company’s projected net

revenues over the coming year.

Strategic Modeling System, Tactical Supply Chain Modeling System, DemandForecasting and Order Management System

The Strategic Modeling System assists senior management in determining the most

effective long-term configuration of the company’s supply chain network. Its models

analyze decisions about major resource acquisitions and divestments, and the manufacture

and distribution of new and existing products over the coming years. The implications of

these decisions to next year’s tactical plans are passed to the Tactical Modeling System

as depicted in Figure 2.7. Such data might include new facilities that will be available or

products to be manufactured, distributed and sold during that time frame. The Tactical

Modeling System provides detailed feedback to the Strategic System about how these

facilities will be used and how market demand will be met over the first year of a strategic

planning horizon.

The Demand Forecasting and Order Management System provides medium and

long term demand forecasts to the Tactical and Strategic Supply Chain Modeling

Systems. Conversely, the Strategic Supply Chain Modeling System provides the Demand

Forecasting System with feedback about the profitability of existing and new product

lines. This information can be used to develop marketing strategies for increasing sales of

profitable products. In fact, the Demand Forecasting System might well be extended to

include marketing models for achieving this end.23

Balancing Centralized and De-Centralized Decision-Making

An important underlying purpose of the System Hierarchy is to resolve

management's conundrum of wishing to make supply chain decisions in both a centralized

and a de-centralized manner24 Centralized decision-making is needed to realize efficiencies

stemming from integration. De-centralized decision-making is needed for rapid, detailed

execution of operations. As we discussed extensively above, the conflict can be resolved

by passing guidelines based on centralized planning using a Modeling System to a lower

level Modeling System.


Demand Forecastingand Order

ManagementSystem

StrategicSupply Chain

Modeling System

TacticalSupply Chain

Modeling System


Details ofnext year's

strategy

Forecasts

Productmix

strategies

Supply chain network configuration

Major resources

New product strategies

Forecasts

Productmix

strategies

Linkages Among Strategic Supply Chain Modeling System,Tactical Chain Modeling System and Demand Forecasting and

Order Management Systems

Figure 2.7


For example, the supply chain manager uses the Tactical Optimization Modeling

System to determine short-term production targets for each plant. These targets are

passed as inputs to the Production Planning Optimization Modeling Systems, one for

each plant, which product managers use to determine more detailed plans, including a

master schedule and optimal capacity levels, for the plant to follow over the next quarter.

These plans in turn are passed to the Production Scheduling and MRP Systems, which

lower level managers use to determine a detailed implementation plan for the next month.

In addition, the lower level systems provide feedback to the high level systems about

necessary adjustments to the centralized plans made necessary by the realities of more

detailed operations.

2.5 LEGACY SYSTEMS AND LEGACY THINKING

Legacy planning systems are outdated computer systems passed on to IT

personnel and managers who employ artistry in trying to apply them to planning

problems that have changed, sometimes using awkward data linkages to new systems.

ERP systems allow a company to replace inefficient legacy systems as well as to

homogenize and integrate disparate corporate databases. Our interest here is to discuss

issues connected with replacing legacy modeling systems and improving legacy thinking

about how supply chain decisions should be made. While the effort required to replace a

legacy modeling system may be significant, overcoming the barriers due to legacy thinking

about supply chain decision-making is often a more difficult task.

In some instances, the legacy modeling system to be updated resides on a

mainframe computer. The company wishes to replace it with a modeling system residing

on a PC, which requires implementing programs that download data and upload plans

identified by the system. Such was the case for a company that manufacturers and

distributes food products that sought recently to replace a legacy system constructed in

the 1970’s. The system used mainframe modeling generation and optimization software

that had been successfully applied for over twenty years. Nevertheless, the company

wished to replace it because model generation time on the mainframe, due to inefficient

data acquisition programs and the need to share CPU time with other users was excessive.


Tests with several PC-based, off-the-shelf modeling systems indicated that the

legacy system could easily be replaced by a better performing system. However, the

company showed no interest in expanding the scope of analysis provided by the model,

which, by 1970’s standards was sophisticated, but by those of the 1990’s, was

simplistic. Specifically, the legacy model sought to minimize the total cost of meeting

demand by assigning production to plants, making inter-plant shipments as needed, and

by sole sourcing markets with shipments from a unique plant. Descriptions of production

costs, capacities and transformation activities in the legacy model were simplistically

described by unit costs for each product at each plant and overall plant capacity. The

legacy model did not address fixed costs, capacity planning and economies of scale

associated with each of several important stages of manufacturing at each plant.

Moreover, it did not address decisions regarding the installation of additional capacity, or

the retirement of excess capacity, at new or existing plants.

In short, over the course of 20 years, use of the legacy system induced legacy

thinking in the company about integrated planning of its supply chain. For the reasons

just indicated, the legacy model produced plans that were probably seriously sub-

optimal. No one in the company was motivated to question current processes or to spend

time collecting data and making model runs to evaluate potentially better ways to manage

its supply chain. It is telling that the legacy system had been designed and implemented

by a corporate operations research group that gradually disappeared from the company,

leaving operations personnel without internal resources for evaluating new decision

processes and models. The operations managers could have sought outside help, but it

was unclear which software vendors or consultants to trust. Moreover, an exercise to

evaluate better models and modeling systems appeared expensive, although an improved

model and modeling system would have paid for itself several times over in the first year

of use.

As another example, a pharmaceutical company contacted a modeling practitioner

because it wished to model a critical production planning step in the manufacture of a

very successful product, with sales in the hundreds of millions of dollars, made from a

natural ingredient of varying quality. The step involving blending the natural ingredient to


produce the basic component used in subsequent manufacturing steps. Approximately 40

blends per year are made.

The company sought a modeling system that would automate and optimize a

planning process that was previously performed manually by a key employee. According

to legacy thinking about how to plan the blends, which we will describe very shortly, this

employee did an excellent job. Nevertheless, the company wished to automate the

process so that others could perform it; they also believed that it could be improved by

computer-based optimization.

Because yearly planning of the blends of natural ingredient had been done

manually, the goal in selecting them was to take the available pool of the natural ingredient

and try to find the set of blends that yielded the most uniform product. This problem

resembles blending of petroleum products, with the added qualitative concern that

product quality would be closely scrutinized by the Federal Drug Administration. In

addition, the blending constraints were defined relative to the composition of the pool,

rather than absolute constraints, which are imposed on petroleum products.

Since the natural ingredient is expensive to acquire, the practitioners suggested that

the company should expand the analysis to more carefully decide upon the amount of its

yearly purchases and more carefully control its inventories. In addition, rather than select

all 40 blends for the product at the beginning of each year, it was suggested that

production plans for each month could be modified according to marketing requirements.

Moreover, within limits, the optimization could select the pool of the natural ingredient

to be used for a given year as well as to optimize the blends made from it. These

suggestions fell on deaf ears. As a result, a model was designed and implemented that did

no more than optimize the manual process. The company was very pleased with the

resulting system and the blends it produced, which were superior to those produced by

hand.

The examples just cited of a company’s reluctance to go beyond legacy thinking in

managing its supply chain, are not exceptional. Although we could speculate further about

the reasons for this reluctance, we must recognize that it is still early days for the

development and use of modeling systems. Many companies are still grappling with ERP

system developments that they feel must proceed the development of analytical tools.


Moreover, we must continue to educate students and managers about the value of rational

decision-making and the form, function, and benefits of models and modeling systems.

2.6 FINAL THOUGHTS

The growth in IT investment depicted in Figure 2.1 suggests that the information

revolution accelerated significantly in the 1990’s. Looking at the figure, one is also

tempted to conclude that the revolution hardly began before 1980. Unfulfilled

expectations about ERP systems and e-commerce indicate that developers and

consultants are still struggling with advances in software and business process redesign

needed to foster efficient and flexible systems for the purposes of Transactional IT. At

the same time, because managers have begun to recognize the need for Analytical IT, ERP

system companies are actively seeking to add optimization modeling systems to their

suite of offerings. Our discussion in this chapter is intended to set the stage for an in-

depth examination of optimization models and modeling systems that serve as the

“brains” of Analytical IT systems for supply chain management.

Furthermore, we recommend that Transactional IT developers pay more attention

to data requirements induced by modeling systems. New software is needed for creating

and applying supply chain decision databases that sit between corporate databases and

modeling systems. Transactional data that is irrelevant to decision-making should be

separated from transactional data that is relevant. Relevant data may require descriptive

analysis transformation before it can be used directly in an optimization model.

Despite 50 years of study in operations research models and methods, plus

numerous examples of successful modeling system implementations, we are still in the

early days of applying them in a pervasive and enduring manner to a range of supply

chain applications. As we shall attempt to demonstrate, operations research academics

and practitioners have filled their intellectual warehouse with models and methods that

offer great promise. The opportunity to pick good ideas from this warehouse and apply

them is exciting. At the same time, given the red hot pace of IT developments, modeling

practitioners must strive to fend off and displace mediocre solutions that, in the confusion

of the IT revolution, are being oversold to managers.


2.7 EXERCISES

In addition to the exercises given below, modeling exercises involving data files and

discussion exercises involving white papers may be found on the book’s web site.

1. Inventory management is inherently an operational planning problem involving

decisions about when to order replacement stock and how much to order when such

decisions are made. Discuss reasons and situations in which inventory management is

also a tactical or strategic planning problem. In addition, discuss ways that inventory

decisions at the three levels of planning, operational, tactical, and strategic, are linked.

2. For a firm that manufactures industrial products, such as industrial chemicals or

printed circuit boards, describe conditions when it is appropriate to pursue

operational plans that maximize net revenue. Your discussion should include reference

to the operational time frame for such decision making, and to processes for

implementing plans that seek to maximize net revenues.

3. In their book, Reengineering the Corporation, Hammer and Champy state the

following25

“To recognize the power inherent in modern information technology and to

visualize its application requires that companies use a form of thinking that

businesspeople usually don’t learn and with which they may feel uncomfortable.

Most executives and managers know how to think deductively. That is, they are

good at defining a problem or problems, then seeking and evaluating different

solutions to it. But applying information technology to business reengineering

demands inductive thinking – the ability to first recognize a powerful solution

and then seek the problems it might solve, problems the company probably

doesn’t even know it has.”


a) According to the dictionary, deduction is the process of reasoning in which a

conclusion follows necessarily from the premises; reasoning from the general to

the specific.26 Moreover, induction is the process of deriving general principles

from particular facts or instances.27 Would you say that these terms were used

correctly in the above statement? Support your answer.

b) Provide arguments citing examples that support the intention of the statement

that management must explore opportunities for reengineering the corporation

to fully exploit new information technology.

c) Provide arguments citing examples where (so-called) inductive approaches have

proven counterproductive.

d) Provide a summary describing the extent to which you agree or disagree with the

statement.

4. In his book, A Primer on Decision Making, March describes rational decision

making as based on answers to four questions28

i) The question of alternatives: What actions are possible?ii) The question of expectations. What future consequences might follow

from each alternative? How lilkely is each possible consequence,assuming that alternative is chosen?

iii) The question of preferences: How valuable (to the decision maker) arethe consequences associated with each of the alternatives?

iv) The question of the decision rule: How is a choice to be made fromamong the alternatives in terms of the values of their consequences?

Proponents of rational decision making as a guiding force in managing companies

and organizations have modified their original thinking to one of bounded

rationality. They still believe that decision makers intend to be rational, but

most decision makers are limited by their mental capacities and the accuracy and

completeness of the information they have gathered. Specifically, they face

serious limitations in attention, memory, comprehension, and communication.

a) To what extent do models actualize and mechanize the theory of rational

decision making?


b) In your opinion, has the information revolution, including the advent of ERP

systems and e-commerce, relieved or exacerbated the limitations on making

rational decisions?

c) How can descriptive and normative models be used to overcome human

limitations of attention, memory, comprehension, and communication?

5. In section 1.3, we discussed Porter’s value chain and remarked that the

intersection of primary and support activities displayed in Figure 1.3 suggest

the need for a new type of matrix organization based on data and models.

Elaborate on this observation with particular reference to the hierarchy of

supply chain systems and the managers who use them discussed in section 2.3.


2.7 NOTES

1 Lohr [1997].

2 This figure is from Lohr [1999] who examines Internet developments through the endof 1999. He suggests that “..it is probably too early to judge whether an Internetrevolution is truly under way. Historians say the Internet should be viewed mainly asthe latest advance in communications, a successor to the telegraph and the telephone,more a technological step than a leap forward.”

3 Vossen [1992] provides a tutorial of database management principles and a historicalperspective on developments up to the early 1990’s.

4 Robinson and Dilts [1999] give an overview of developments in ERP systems with anemphasis on the role that operations research models can play in extendingthesesystems to analyze supply chain decisions.

5 These figures are quoted in Deutsch [1998] who describes the pain that manycompanies have felt in trying to implement ERP systems.

6 Limitations as well as benefits of ERP systems are discussed in more detail byRobinson and Dilts [1998].

7 Latamore [1999] reports on developments in ERP systems, including extensions toWeb-enabled versions.

8 Canedy [1998] reviews business-to-consumer developments including projections oftotal Internet business in several markets by the year 2002, which are generally quitesmall relative to store sales. Of course, Internet developments far beyond 2002 arestill shrouded in considerable mystery.

9 See Tedeschi [1999].

10 Foster [1999] quotes this figure, attributing it to Forrester Research in Cambridge,Massachusetts.

11 See Foster [1999, 20].

12 Economist [2000] contains a discussion of the pros and cons of achieving perfectlyefficient markets over the Internet.

13 Starting in 2000, SAP, the leading ERP system company, will offer a suite ofmodeling modules to complement its ERP modules. Two other of the top five ERP


companies, J. D. Edwards and Baan, acquired smaller companies with supply chainmodeling systems.

14 For more information about MRP Systems, see Baker [1993] and Sipper and Bulfin[1997; 337-363].

15 For more information about DRP Systems, see Stegner [1994].

16 Demand forecasting is discussed in detail in § 6.7.

17 Shapiro [1993] reviews a variety of optimization models for production scheduling. Aproduction scheduling model and solution methodology is proposed in detail in § 5.3and § 5.5.

18 See Golden and Assad [1988] or Crainic and Laporte [1998] for a broad treatment ofvehicle routing algorithms and applications, and Hall and Partyka [1997] for a surveyof off-the-shelf packages for vehicle routing. A vehicle routing model and solutionmethodology is proposed in detail in § 5.2 and § 5.4.

19 Thomas and McClain [1993] provide a comprehensive literature survey of productionplanning models through the early 1990’s. Two examples of implemented productionplanning optimization modeling systems are those developed at Harris Corporation(Leachman et al [1996]) and Sadia (Taube-Netto [1996]). The applications at HarrisCorporation are discussed in detail in §10.5.

20 Tactical supply chain models and modeling systems are discussed in Chapters 7 and8.

21 Strategic supply chain models and modeling systems are discussed in Chapters 7 and8.

22 Graves [1982] gives an example of an optimization model for which aggregation anddisaggregation between short-term tactical and operational production scheduling arerigorously defined. For most applications, such rigor might be difficult to achieve.Nevertheless, practitioners would do well to push formal models and methods beforeresorting to ad hoc methods.

23 The integration of marketing and supply chain models is discussed in Chapter 8.

24 The notion that IT promotes schemes for simultaneous centralized and de-centralizedplanning in the firm was suggested by Hammer and Champy [1993; 93], althoughthey are vague about how it could actually be accomplished.

25 See Hammer and Champy [1993, 84].


26 See The American Heritage College Dictionary [1993, 362].

27 See The American Heritage College Dictionary [1993, 693].

28 See March [1994, 2-3].


2.8 REFERENCES

Baker, K. R. [1993], “Requirements Planning,” Chapter 11 in Handbooks in OperationsResearch and Management Science: Logistics of Production and Inventory, edited by S. C.Graves, A. H. G. Rinnoy Kan, P. H. Zipkin, North-Holland.

Canedy D. [1998], “Need Aspargus? Just Click It,” New York Times, C1, September 10,1998.

Crainic, T. G. and G. Laporte, editors [1998], Fleet Management and Logistics, KluwerAcademic.

Deutsch, C. H. [1998], “Software That Can Make a Grown Company Cry,” New YorkTimes, November 18.

Economist [2000], “How to be Perfect,” 82, February 12.

Foster, T. A. [1999], “Global eProcurement Solutions,” Supply Chain ManagementReview Global Supplement, 19-22, Spring.

Golden, B. L. and A. A. Assad, editors, Vehicle Routing: Methods and Studies, North-Holland, 1988

Graves, S. C. [1982], ‘Using Lagrangean Techniques to Solve Hierarchical ProductionPlanning Problems,” Management Science, 28, 260-275.

Hall, R. W. and J. G. Partyka, "On the Road to Efficiency," OR/MS Today, 24 (1997), 3,38-47.

Hammer, M. and J. Champy [1993], Reengineering the Corporation, HarperBusiness.

Latamore, G. B. [1999], “ERP in the New Millennium,” APICS, 9, No. 6, 28-32.

Leachman, R. C., R. F. Benson, C. Liu and D. J. Raar, "IMPReSS: An AutomatedProduction-Planning and Delivery-Quotation System at Harris Corporation-Semiconductor Sector," Interfaces, 26 (1996), 1, 6-37.

Lohr, S. [1997], “Information Technology Field is Rated Largest U.S. Industry,” NewYork Times, November 18.

Lohr, S. [1999], “The Economy Transformed, Bit by Bit,” New York Times, December20.

Robinson, A. G. and D. M. Dilts [1999], “OR & ERP,” ORMS Today, 26, No. 3, 30-35.


Shapiro, J. F. [1993], "Mathematical programming models and methods for productionplanning and scheduling," Chapter 8 in Graves, S. C., A. H. G. Rinooy Kan and P. H.Zipkin, North-Holland.

Sipper, D. and R. L. Bulfin, Jr. [1997], Production: Planning, Control, Integration,McGraw-Hill.

Stenger, A. J. [1994], “Distribution Resource Planning,” Chapter 17 in The LogisticsHandbook, edited by J. F. Robeson and W. C. Copacino, The Free Press.

Taube-Netto, M., "Integrated Planning for Poultry Production at Sadia," Interfaces, 26(1996), 38-53.

Tedeschi, R. [1999], “E-Commerce Report,” New York Times, C4, September 27, 1999.

Thomas, L. J. and J. O. McClain [1993], "An Overview of Production Planning," Chapter7 in Handbooks in Operations Research and Management Science: Logistics of Productionand Inventory, edited by S. C. Graves, A. H. G. Rinnoy Kan, P. H. Zipkin, North-Holland.

Vossen, G. [1992], “Databases and Database Management,” Chapter 4 in Handbooks inOperations Research and Management Science – Volume 3: Computing, edited by E. G.Coffman, Jr., J. K. Lenstra, A. H. G. Rinooy Kan.

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CHAPTER TWO INFORMATION · PDF fileCHAPTER TWO INFORMATION TECHNOLOGY ... not just supply chain management. ... companies in an extended or virtual supply chain. In §2.3,